Viral Kinetics Model of SARS-CoV-2 Infection Informs Drug Discovery, Clinical Dose, and Regimen Selection.

Area of Science:

• Pharmacology
• Virology
• Computational Biology

Background:

• Quantitative systems pharmacology (QSP) is crucial for predicting the safety and efficacy of SARS-CoV-2 therapeutics.
• Understanding virus-host interactions is key to developing effective antiviral strategies.

Purpose of the Study:

• To develop and validate a QSP modeling framework for predicting responses to SARS-CoV-2 antiviral therapeutics with distinct mechanisms of action.
• To guide the dose selection for novel therapeutics like ensovibep based on in silico predictions.

Main Methods:

• Parameterized three distinct QSP models for cell entry inhibitors, anti-replicatives, and neutralizing biologics using COVID-19 viral kinetics data.
• Validated model predictions against clinical data for nirmatrelvir/ritonavir (Paxlovid®) and bamlanivimab/casirivimab/imdevimab (REGEN-COV®).
• Developed a novel in silico metric, area under the curve (AUC) of free spike protein, to assess antiviral activity.

Main Results:

• Neutralizing biologic and anti-replicative mechanisms demonstrated the most rapid and robust antiviral activity in silico.
• QSP model predictions showed good agreement with observed viral load reduction for existing antivirals.
• In silico predictions for ensovibep informed Phase 2 clinical trial dose levels (75, 225, 600 mg).

Conclusions:

• The QSP modeling framework effectively predicts antiviral responses and guides therapeutic development for SARS-CoV-2.
• Model predictions for ensovibep showed good agreement with subsequent clinical trial patient data.
• This approach demonstrates the utility of QSP in optimizing the development of novel antiviral agents.